8292432 Urinary phthalate metabolite concentrations by occupation and industry: evidence from the Canadian Health measures survey
Bibliographic record
Abstract
Objective Phthalates are ubiquitous endocrine-disrupting compounds linked to numerous adverse health outcomes, including hormone-sensitive cancers. Previous studies of phthalates, such as the widely used plasticizer di-2-ethylhexylphthalate (DEHP), have identified important environmental determinants, but few have examined their association with occupational factors. We sought to identify occupations with elevated urinary phthalate biomarker concentrations in a nationally-representative biomonitoring study of adults in Canada. Methods We conducted a cross-sectional study of 4,180 individuals aged 20-80 years from the Canadian Health Measures Survey (CHMS) between 2007-2011 (cycles 1-2) and 2016-2019 (cycles 5-6). Spot urine samples were analyzed for concentrations of 11 phthalate metabolites quantified using chromatography and spectrometry. Creatinine-corrected molar sum groups were constructed to describe DEHP and high- and low- molecular weight phthalate (HMWP & LMWP, respectively) exposures. Linear regression models estimated predicted least-squares adjusted geometric means (GMs) and geometric mean ratios by occupation and industry groups. Models were adjusted for dietary, sociodemographic, and anthropometric factors. Results Phthalate concentrations (nmol/g creatinine) were elevated among women, older adults, individuals of Asian ethnicities, smokers, urban residents, frequent consumers of grain products, and those in the earliest CHMS cycles. By occupation, LMWP concentrations were highest among labourers in processing, manufacturing, and utilities (GM[LMWP] = 559.92; 95% CI: 333.03-941.40). Workers in these occupations were commonly employed in clothing, electrical, and machinery manufacturing industries. HMWP and DEHP concentrations were highest in trades helpers, construction labourers, and related occupations (GM[HMWP] = 124.94; 95% CI: 88.10-177.18) as well as workers in natural resources, agriculture, and related production (GM[DEHP] = 64.10; 95% CI: 46.39-88.56), respectively. Among women, elevated LMWP and DEHP concentrations were observed in installers, repairers, servicers, and material handlers. Conclusion Results suggest heterogeneity in phthalate exposure by occupation and sex. Findings will be used to inform subsequent investigations of breast and prostate cancer risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".